The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry

Pandini, A., Fraccalvieri, D., Bonati, L. (2013). Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry. CURRENT TOPICS IN MEDICINAL CHEMISTRY, 13(5), 642-651 [10.2174/1568026611313050007].

Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry

FRACCALVIERI, DOMENICO;BONATI, LAURA
2013

Abstract

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry
Articolo in rivista - Articolo scientifico
Artificial neural networks, Self-organising maps, Protein conformational ensembles, Molecular dynamics, Molecular docking, Drug-design
English
2013
13
5
642
651
none
Pandini, A., Fraccalvieri, D., Bonati, L. (2013). Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry. CURRENT TOPICS IN MEDICINAL CHEMISTRY, 13(5), 642-651 [10.2174/1568026611313050007].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/43815
Citazioni
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 17
Social impact